System

Abstract

An object of the system according to the embodiment is to realize personalized coordination proposal, real-time stock confirmation, and multilingual support in an apparel shop.SOLUTION: A system according to an embodiment includes a generation AI, an emotion-recognizing humanoid robot, a coordination suggestion unit, an inventory check unit, a substitute item suggestion unit, a multilingual handling unit, and a data collecting unit. The generation AI understands texts, images, sounds, and videos. The emotion recognition humanoid robot recognizes an emotion of a user. The coordination proposal unit analyzes the user's preference and past purchase history and proposes optimal coordination. The stock confirmation unit analyzes the stock data of the store in real time to confirm the stock status. The substitute commodity proposal unit proposes a substitute commodity when the commodity is not in stock. The multilingual adaptation unit performs multilingual adaptation. The data collection unit collects and analyzes user's action data and purchase history.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately address personalized coordination suggestions, real-time inventory checks, or multilingual support in apparel shops, so there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized coordination suggestions, real-time inventory confirmation, and multilingual support in apparel shops. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, an emotion-recognition humanoid robot, a coordination suggestion unit, an inventory confirmation unit, an alternative product suggestion unit, a multilingual support unit, and a data collection unit. The generation AI understands text, images, audio, and video. The emotion-recognition humanoid robot recognizes the user's emotions. The coordination suggestion unit analyzes the user's preferences and past purchase history to suggest optimal coordination. The inventory confirmation unit analyzes store inventory data in real time to check stock status. The alternative product suggestion unit suggests alternative products if an item is out of stock. The multilingual support unit provides multilingual support. The data collection unit collects and analyzes user behavioral data and purchase history. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized coordination suggestions, real-time inventory confirmation, and multilingual support in apparel shops. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The personalized service system according to an embodiment of the present invention combines generative AI and emotion-recognition humanoid robots to provide personalized services in various fields. This personalized service system can improve user satisfaction by suggesting optimal outfits based on the user's preferences and past purchase history, checking inventory status in real time, and suggesting alternative products.

[0029] A personalized service system according to an embodiment includes a generation AI, an emotion-recognition humanoid robot, a coordination suggestion unit, an inventory confirmation unit, an alternative product suggestion unit, a multilingual support unit, and a data collection unit. The generation AI is a multimodal generative AI model that understands text, images, audio, and video. It analyzes user requests and provides appropriate services. For example, the generation AI generates coordinations based on prompts containing the user's request. The emotion-recognition humanoid robot recognizes the user's emotions and provides appropriate feedback. For example, the emotion-recognition humanoid robot analyzes the user's facial expressions and voice to estimate their emotions. The coordination suggestion unit analyzes the user's preferences and past purchase history to suggest optimal coordinations. For example, if a user requests a "casual style," the generation AI analyzes the request and selects appropriate items. The inventory confirmation unit analyzes store inventory data in real time to check stock availability. For example, if a user asks, "Do you have this shirt in size M?", the generation AI checks the inventory data. If the item is out of stock, it suggests, "Size M is out of stock, but how about size L or another design?" The substitute product suggestion unit suggests alternative products when they are out of stock. For example, if a product desired by a user is out of stock, the generation AI suggests similar products. The multilingual support unit handles multiple languages. For example, if a user makes a request in English or Chinese, the generation AI will understand the request and provide appropriate service. The data collection unit collects and analyzes user behavior data and purchase history. For example, it analyzes data such as which items are popular and what time of day customers visit the store most often, and plans effective promotions. This allows the personalized service system to suggest optimal outfits based on the user's preferences and past purchase history, check stock availability in real time, and suggest alternative products, thereby improving user satisfaction.

[0030] The coordination suggestion unit can suggest the optimal coordination based on the user's body type, skin color, and season. For example, when a user uploads an image of themselves, the generation AI analyzes the image and suggests coordination that suits the user's body type. For example, it selects jackets and pants that fit the body type. The generation AI also suggests coordination that matches the user's skin color. For example, it selects items in colors that match the skin color. Furthermore, the generation AI suggests coordination based on the season. For example, it suggests items made of warm materials in winter. This makes it possible to improve user satisfaction by suggesting the optimal coordination based on the user's body type, skin color, and season.

[0031] The coordination suggestion unit can suggest coordinations based on the user's past purchase history and social media activity. For example, the coordination suggestion unit analyzes the user's past purchase history and suggests items from the same brand or style. For example, it can suggest new items from a brand that was previously purchased. The generation AI also analyzes the user's social media activity to understand the user's preferences. For example, it can suggest items that match the user's preferences based on items the user has "liked" and posts they have shared. This makes it possible to improve user satisfaction by suggesting coordinations based on the user's past purchase history and social media activity.

[0032] The inventory confirmation unit can provide the expected arrival date of the product and the stock status of other stores in real time. For example, if a user asks, "Do you have this shirt in size M?" and the product is out of stock, the generation AI will check the expected arrival date of the product and suggest, "Size M is out of stock, but will be in stock next week." The generation AI will also check the stock status of other stores and suggest, "This store is out of stock, but other stores have it in stock." This makes it possible to improve user satisfaction by providing the expected arrival date of the product and the stock status of other stores in real time.

[0033] The inventory confirmation unit can suggest alternative products based on the user's past purchase history and preferences. For example, if the user asks, "Do you have this shirt in size M?" and the item is out of stock, the generation AI will analyze the user's past purchase history and suggest, "Size M is out of stock, but how about a new shirt from a brand you previously purchased?" The generation AI also analyzes the user's preferences and suggests new or related products that the user might be interested in. For example, if the product the user wants is out of stock, the generation AI will suggest a similar product. This makes it possible to improve user satisfaction by suggesting alternative products based on the user's past purchase history and preferences.

[0034] The multilingual support unit can support not only the user's native language but also regional dialects and slang. For example, when a user requests "I need a jacket for the winter" in English, the generation AI will take into account the regional dialect and slang and suggest "How about this cozy winter jacket?" The generation AI can also support regional dialects and slang in addition to the user's native language. For example, if a product desired by the user is out of stock, the generation AI can suggest an alternative product using regional dialects and slang. This improves user satisfaction by supporting not only the user's native language but also regional dialects and slang.

[0035] The multilingual support unit can make service suggestions that take into account the user's cultural background and customs. For example, when a user makes a request in English saying, "I am looking for a traditional outfit," the generation AI will take that cultural background into account and suggest, "How about this traditional kimono?" The generation AI also makes service suggestions that take into account the user's cultural background and customs. For example, if a product the user wants is out of stock, the generation AI will suggest an alternative product, taking into account the user's cultural background and customs. In this way, service suggestions that take into account the user's cultural background and customs can improve user satisfaction.

[0036] The data collection unit collects not only user behavioral data, but also social media activity and online reviews, allowing for the development of comprehensive marketing strategies. The data collection unit collects, for example, users' activities on social media, such as "likes" and "shares," and reflects this in marketing strategies. For example, promotions are carried out based on popular posts. The generation AI also analyzes users' online reviews to understand their preferences and opinions. For example, it suggests products that match the user's preferences based on reviews posted by the user. In this way, by collecting not only user behavioral data, but also social media activity and online reviews, and developing comprehensive marketing strategies, effective promotions can be achieved.

[0037] The data collection unit can introduce anonymization technology to protect user privacy. For example, when collecting user data, the data collection unit introduces anonymization technology to prevent individuals from being identified. For example, user IDs are randomly generated to protect personal information. In addition, the generation AI uses data masking and pseudo-anonymization technology to protect user privacy. By introducing anonymization technology to protect user privacy, an environment can be created in which data can be provided with peace of mind.

[0038] The data collection unit collects data not only from apparel shops, but also from restaurants and entertainment facilities, allowing for the development of marketing strategies specialized for each industry. For example, the data collection unit collects user order histories at restaurants and reflects this in marketing strategies. For example, promotions are carried out based on popular menu items. The generation AI also collects user behavior data at entertainment facilities and reflects this in marketing strategies. For example, events that match the user's preferences are suggested based on admission data. This allows for the development of marketing strategies specialized for each industry, allowing for effective promotions.

[0039] The data collection unit can provide incentives to increase users' purchasing motivation. For example, the data collection unit can award points when a user purchases a specific product, which can be used on the next purchase. For example, points can be awarded according to the purchase amount. In addition, the generation AI can provide discount coupons and benefits to increase users' purchasing motivation. For example, a user who purchases a specific product can be provided with a coupon that can be used on the next purchase. In this way, by providing incentives to increase users' purchasing motivation, effective promotions can be realized.

[0040] An emotion-recognition humanoid robot can suggest the optimal menu based on the user's past order history and preferences. For example, when a user requests, "Tell me what snacks go well with beer," the generation AI of the emotion-recognition humanoid robot will analyze the user's past order history and suggest, "How about the fried chicken and edamame you ordered previously?" The generation AI will also analyze the user's preferences and suggest menu items that the user might be interested in. For example, if the user's desired menu item is not available, the generation AI will suggest a similar menu item. This makes it possible to improve user satisfaction by suggesting the optimal menu based on the user's past order history and preferences.

[0041] An emotion-recognizing humanoid robot can make optimal property suggestions based on the user's past search history and preferences. For example, when a user requests, "I'm looking for a 2LDK property near the station," the generation AI analyzes the user's past search history and suggests, "Among the properties you previously searched for, how about a 2LDK property near the station?" The generation AI also analyzes the user's preferences and suggests properties that the user might be interested in. For example, if the property the user desires is not available, the generation AI will suggest similar properties. This makes it possible to improve user satisfaction by making optimal property suggestions based on the user's past search history and preferences.

[0042] An emotion-recognizing humanoid robot can suggest optimal learning methods based on the user's past learning history and preferences. For example, when a user requests help with their math homework, the generative AI analyzes the user's past learning history and suggests, "It would be a good idea to solve this problem using the method you learned before." The generative AI also analyzes the user's preferences and suggests learning methods that the user might be interested in. For example, if the user's desired learning method is not available, the generative AI can suggest an alternative learning method. This can increase the user's motivation to learn by suggesting optimal learning methods based on the user's past learning history and preferences.

[0043] An emotion-aware humanoid robot can monitor a user's learning progress in real time and adjust the learning plan as needed. For example, when a user requests help with their math homework, the generative AI will monitor the user's learning progress and suggest, "To solve this problem, first review this basics." The generative AI can also monitor the user's learning progress in real time and adjust the learning plan as needed. For example, if the user is struggling with a particular problem, the generative AI will suggest reviewing basic knowledge related to that problem. This allows the system to monitor the user's learning progress in real time and adjust the learning plan as needed, enabling effective learning.

[0044] An emotion-recognizing humanoid robot can provide a customized learning plan tailored to the user's learning style. For example, when a user requests "Help me with my math homework," the generative AI analyzes the user's learning style and suggests, "Let's use a visual explanation to solve this problem." The generative AI can also provide a customized learning plan tailored to the user's learning style. For example, it can provide explanations using diagrams and graphs to visual users, and audio commentary to auditory users. This allows for effective learning by providing a customized learning plan tailored to the user's learning style.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The personalized service system can also collect the user's health data and suggest outfits based on their health condition. For example, if the user is connected to a health management app, the generation AI can analyze that data and suggest outfits that suit the user's physical condition. For example, if the user gets tired easily, it can suggest items made from relaxing materials. Also, if the user has allergies, it can suggest items made from materials that do not contain allergens. This can improve user satisfaction by suggesting outfits based on the user's health condition.

[0047] The outfit suggestion unit can suggest the best outfit for the user's body type, skin color, and season. For example, when a user uploads an image of themselves, the generation AI analyzes the image and suggests outfits that suit the user's body type. For example, it selects jackets and pants that fit the body type. The generation AI also suggests outfits that match the user's skin color. For example, it selects items in colors that match the skin color. Furthermore, the generation AI suggests outfits that match the season. For example, it suggests items made of warm materials in winter. This makes it possible to improve user satisfaction by suggesting the best outfits for the user's body type, skin color, and season.

[0048] The coordination suggestion unit can suggest coordinations based on the user's past purchase history and social media activity. For example, it can analyze the user's past purchase history and suggest items from the same brand or style. For example, it can suggest new items from a brand that was previously purchased. The generation AI also analyzes the user's social media activity to understand the user's preferences. For example, it can suggest items that match the user's preferences based on items the user has "liked" and posts they have shared. This can improve user satisfaction by suggesting coordinations based on the user's past purchase history and social media activity.

[0049] The inventory confirmation unit can provide the expected arrival date of a product and the stock status of other stores in real time. For example, if a user asks, "Do you have this shirt in size M?" and it is out of stock, the generation AI will check the expected arrival date of the product and suggest, "Size M is out of stock, but will be in stock next week." The generation AI will also check the stock status of other stores and suggest, "This store is out of stock, but other stores have it in stock." This allows for improved user satisfaction by providing the expected arrival date of a product and the stock status of other stores in real time.

[0050] The inventory confirmation unit can suggest alternative products based on the user's past purchase history and preferences. For example, if a user asks, "Do you have this shirt in size M?" and it is out of stock, the generation AI will analyze the user's past purchase history and suggest, "Size M is out of stock, but how about a new shirt from a brand you previously purchased?" The generation AI also analyzes the user's preferences and suggests new or related products that the user might be interested in. For example, if the product the user wants is out of stock, the generation AI will suggest a similar product. This makes it possible to improve user satisfaction by suggesting alternative products based on the user's past purchase history and preferences.

[0051] The multilingual support unit can handle not only the user's native language but also regional dialects and slang. For example, when a user requests "I need a jacket for the winter" in English, the generation AI will take into account the regional dialect and slang and suggest "How about this cozy winter jacket?" The generation AI also handles not only the user's native language but also regional dialects and slang. For example, if the product the user wants is out of stock, the generation AI will suggest an alternative product using regional dialects and slang. This improves user satisfaction by supporting not only the user's native language but also regional dialects and slang.

[0052] The multilingual support unit can make service suggestions that take into account the user's cultural background and customs. For example, when a user makes a request in English saying, "I am looking for a traditional outfit," the generation AI will take that cultural background into account and suggest, "How about this traditional kimono?" The generation AI also makes service suggestions that take into account the user's cultural background and customs. For example, if the product the user wants is out of stock, the generation AI will suggest an alternative product, taking into account the user's cultural background and customs. This makes it possible to improve user satisfaction by making service suggestions that take into account the user's cultural background and customs.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: Generative AI is a multimodal generative AI model that understands text, images, audio, and video, analyzes the user's request, and provides appropriate services. For example, the generative AI generates a coordinate system based on a prompt containing the user's request. Step 2: The emotion-aware humanoid robot recognizes the user's emotions and provides appropriate feedback. For example, the emotion-aware humanoid robot analyzes the user's facial expressions and voice to infer their emotions. Step 3: The outfit suggestion unit analyzes the user's preferences and past purchase history to suggest the optimal outfit. For example, if a user requests a "casual style," the AI ​​analyzes the request and selects appropriate items. Step 4: The inventory check unit analyzes the store's inventory data in real time to check the stock status. For example, if a user asks, "Do you have this shirt in size M?", the generation AI will check the inventory data and, if it is out of stock, will suggest, "Size M is out of stock, but how about size L or a different design?" Step 5: The substitute product suggestion unit suggests substitute products if they are out of stock. For example, if the product desired by the user is out of stock, the generation AI will suggest similar products. Step 6: The multilingual support section handles multilingual support. For example, if a user makes a request in English or Chinese, the generation AI will understand the request and provide the appropriate service. Step 7: The data collection department collects and analyzes user behavior data and purchase history. For example, it analyzes data such as which items are popular and what time of day people visit the store, and plans effective promotions.

[0055] (Example 2) The personalized service system according to an embodiment of the present invention combines generative AI and emotion-recognition humanoid robots to provide personalized services in various fields. This personalized service system can improve user satisfaction by suggesting optimal outfits based on the user's preferences and past purchase history, checking inventory status in real time, and suggesting alternative products.

[0056] A personalized service system according to an embodiment includes a generation AI, an emotion-recognition humanoid robot, a coordination suggestion unit, an inventory confirmation unit, an alternative product suggestion unit, a multilingual support unit, and a data collection unit. The generation AI is a multimodal generative AI model that understands text, images, audio, and video. It analyzes user requests and provides appropriate services. For example, the generation AI generates coordinations based on prompts containing the user's request. The emotion-recognition humanoid robot recognizes the user's emotions and provides appropriate feedback. For example, the emotion-recognition humanoid robot analyzes the user's facial expressions and voice to estimate their emotions. The coordination suggestion unit analyzes the user's preferences and past purchase history to suggest optimal coordinations. For example, if a user requests a "casual style," the generation AI analyzes the request and selects appropriate items. The inventory confirmation unit analyzes store inventory data in real time to check stock availability. For example, if a user asks, "Do you have this shirt in size M?", the generation AI checks the inventory data. If the item is out of stock, it suggests, "Size M is out of stock, but how about size L or another design?" The substitute product suggestion unit suggests alternative products when they are out of stock. For example, if a product desired by a user is out of stock, the generation AI suggests similar products. The multilingual support unit handles multiple languages. For example, if a user makes a request in English or Chinese, the generation AI will understand the request and provide appropriate service. The data collection unit collects and analyzes user behavior data and purchase history. For example, it analyzes data such as which items are popular and what time of day customers visit the store most often, and plans effective promotions. This allows the personalized service system to suggest optimal outfits based on the user's preferences and past purchase history, check stock availability in real time, and suggest alternative products, thereby improving user satisfaction.

[0057] The outfit suggestion unit can estimate the user's emotions and suggest outfits based on those emotions. For example, if a user inputs, "I'm feeling down today," the generation AI analyzes that emotion and suggests outfits in bright colors to lift the mood. For example, it selects items in bright colors such as yellow and orange. This makes it possible to improve user satisfaction by suggesting outfits based on the user's emotions.

[0058] The coordination suggestion unit can suggest the optimal coordination based on the user's body type, skin color, and season. For example, when a user uploads an image of themselves, the generation AI analyzes the image and suggests coordination that suits the user's body type. For example, it selects jackets and pants that fit the body type. The generation AI also suggests coordination that matches the user's skin color. For example, it selects items in colors that match the skin color. Furthermore, the generation AI suggests coordination based on the season. For example, it suggests items made of warm materials in winter. This makes it possible to improve user satisfaction by suggesting the optimal coordination based on the user's body type, skin color, and season.

[0059] The coordination suggestion unit can suggest coordinations based on the user's past purchase history and social media activity. For example, the coordination suggestion unit analyzes the user's past purchase history and suggests items from the same brand or style. For example, it can suggest new items from a brand that was previously purchased. The generation AI also analyzes the user's social media activity to understand the user's preferences. For example, it can suggest items that match the user's preferences based on items the user has "liked" and posts they have shared. This makes it possible to improve user satisfaction by suggesting coordinations based on the user's past purchase history and social media activity.

[0060] The inventory confirmation unit can estimate the user's emotions and suggest alternative products if the item is out of stock. For example, if the user asks, "Do you have this shirt in size M?" and the item is out of stock, the generation AI will analyze the user's disappointment and suggest, "Size M is out of stock, but how about size L or a different design?" The generation AI can also analyze the user's emotions and suggest alternative products to alleviate negative emotions. For example, if the item the user wants is out of stock, the generation AI will suggest a similar product. This makes it possible to estimate the user's emotions and suggest alternative products if the item is out of stock, thereby improving user satisfaction.

[0061] The inventory confirmation unit can provide the expected arrival date of the product and the stock status of other stores in real time. For example, if a user asks, "Do you have this shirt in size M?" and the product is out of stock, the generation AI will check the expected arrival date of the product and suggest, "Size M is out of stock, but will be in stock next week." The generation AI will also check the stock status of other stores and suggest, "This store is out of stock, but other stores have it in stock." This makes it possible to improve user satisfaction by providing the expected arrival date of the product and the stock status of other stores in real time.

[0062] The inventory confirmation unit can suggest alternative products based on the user's past purchase history and preferences. For example, if the user asks, "Do you have this shirt in size M?" and the item is out of stock, the generation AI will analyze the user's past purchase history and suggest, "Size M is out of stock, but how about a new shirt from a brand you previously purchased?" The generation AI also analyzes the user's preferences and suggests new or related products that the user might be interested in. For example, if the product the user wants is out of stock, the generation AI will suggest a similar product. This makes it possible to improve user satisfaction by suggesting alternative products based on the user's past purchase history and preferences.

[0063] The multilingual support unit can estimate the user's emotions and suggest appropriate words and expressions to elicit positive emotions. For example, when a user requests "I am looking for a formal dress" in English, the generation AI analyzes the emotion and suggests "How about this elegant dress?" to elicit positive emotions. The generation AI also analyzes the user's emotions and suggests appropriate words and expressions. For example, if the product the user wants is out of stock, the generation AI will suggest an alternative product using positive expressions. In this way, by estimating the user's emotions and suggesting appropriate words and expressions to elicit positive emotions, it is possible to improve user satisfaction.

[0064] The multilingual support unit can support not only the user's native language but also regional dialects and slang. For example, when a user requests "I need a jacket for the winter" in English, the generation AI will take into account the regional dialect and slang and suggest "How about this cozy winter jacket?" The generation AI can also support regional dialects and slang in addition to the user's native language. For example, if a product desired by the user is out of stock, the generation AI can suggest an alternative product using regional dialects and slang. This improves user satisfaction by supporting not only the user's native language but also regional dialects and slang.

[0065] The multilingual support unit can make service suggestions that take into account the user's cultural background and customs. For example, when a user makes a request in English saying, "I am looking for a traditional outfit," the generation AI will take that cultural background into account and suggest, "How about this traditional kimono?" The generation AI also makes service suggestions that take into account the user's cultural background and customs. For example, if a product the user wants is out of stock, the generation AI will suggest an alternative product, taking into account the user's cultural background and customs. In this way, service suggestions that take into account the user's cultural background and customs can improve user satisfaction.

[0066] The data collection unit can collect user emotional data and reflect it in marketing strategies. For example, if a user expresses positive emotions toward a particular product, the data collection unit will focus on promoting that product. For example, products with high emotional scores will be used in advertising. The generation AI also analyzes user emotional data and reflects it in marketing strategies. For example, if a product desired by a user is out of stock, the generation AI will suggest an alternative product based on the emotional data. In this way, by collecting user emotional data and reflecting it in marketing strategies, effective promotions can be achieved.

[0067] The data collection unit collects not only user behavioral data, but also social media activity and online reviews, allowing for the development of comprehensive marketing strategies. The data collection unit collects, for example, users' activities on social media, such as "likes" and "shares," and reflects this in marketing strategies. For example, promotions are carried out based on popular posts. The generation AI also analyzes users' online reviews to understand their preferences and opinions. For example, it suggests products that match the user's preferences based on reviews posted by the user. In this way, by collecting not only user behavioral data, but also social media activity and online reviews, and developing comprehensive marketing strategies, effective promotions can be achieved.

[0068] The data collection unit can introduce anonymization technology to protect user privacy. For example, when collecting user data, the data collection unit introduces anonymization technology to prevent individuals from being identified. For example, user IDs are randomly generated to protect personal information. In addition, the generation AI uses data masking and pseudo-anonymization technology to protect user privacy. By introducing anonymization technology to protect user privacy, an environment can be created in which data can be provided with peace of mind.

[0069] The data collection unit collects data not only from apparel shops, but also from restaurants and entertainment facilities, allowing for the development of marketing strategies specialized for each industry. For example, the data collection unit collects user order histories at restaurants and reflects this in marketing strategies. For example, promotions are carried out based on popular menu items. The generation AI also collects user behavior data at entertainment facilities and reflects this in marketing strategies. For example, events that match the user's preferences are suggested based on admission data. This allows for the development of marketing strategies specialized for each industry, allowing for effective promotions.

[0070] The data collection unit can provide incentives to increase users' purchasing motivation. For example, the data collection unit can award points when a user purchases a specific product, which can be used on the next purchase. For example, points can be awarded according to the purchase amount. In addition, the generation AI can provide discount coupons and benefits to increase users' purchasing motivation. For example, a user who purchases a specific product can be provided with a coupon that can be used on the next purchase. In this way, by providing incentives to increase users' purchasing motivation, effective promotions can be realized.

[0071] The data collection unit can use the emotion estimation function to monitor the effectiveness of the marketing strategy in real time and adjust the strategy as necessary. For example, if a user expresses positive emotions toward a particular promotion, the data collection unit will continue that promotion. For example, it will continue a promotion with a high emotion score. The generation AI also monitors the user's emotion data in real time and adjusts the strategy as necessary. For example, if a user expresses negative emotions, it will change the promotion content. In this way, by using the emotion estimation function to monitor the effectiveness of the marketing strategy in real time and adjusting the strategy as necessary, it is possible to achieve effective promotions.

[0072] An emotion-recognizing humanoid robot can estimate a user's emotions in a restaurant and make menu suggestions that elicit positive emotions. For example, when a user requests, "Tell me what snacks go well with beer," the generative AI analyzes the user's emotions and makes positive suggestions, such as, "I recommend fried chicken or edamame with beer." The generative AI can also analyze the user's emotions and make menu suggestions that elicit positive emotions. For example, if the menu item the user desires is not available, the generative AI can suggest an alternative menu item. This allows restaurants to estimate a user's emotions and make menu suggestions that elicit positive emotions, thereby improving user satisfaction.

[0073] An emotion-recognition humanoid robot can suggest the optimal menu based on the user's past order history and preferences. For example, when a user requests, "Tell me what snacks go well with beer," the generation AI of the emotion-recognition humanoid robot will analyze the user's past order history and suggest, "How about the fried chicken and edamame you ordered previously?" The generation AI will also analyze the user's preferences and suggest menu items that the user might be interested in. For example, if the user's desired menu item is not available, the generation AI will suggest a similar menu item. This makes it possible to improve user satisfaction by suggesting the optimal menu based on the user's past order history and preferences.

[0074] An emotion-recognition humanoid robot can suggest the most satisfying menu based on emotion estimation data. For example, when a user requests, "Tell me what snacks go well with beer," the generation AI analyzes the emotion estimation data and suggests, "I recommend fried chicken or edamame with beer." The generation AI also suggests the most satisfying menu based on the user's emotion estimation data. For example, if the menu the user wants is not available, the generation AI will suggest an alternative menu. This makes it possible to improve user satisfaction by suggesting the most satisfying menu based on emotion estimation data.

[0075] In the real estate industry, emotion-recognizing humanoid robots can estimate users' emotions and make property suggestions that elicit positive emotions. For example, when a user requests, "I'm looking for a 2LDK property near the station," the generative AI analyzes the user's emotions and makes a positive suggestion, such as, "This property is a 5-minute walk from the station and has a spacious living room." The generative AI can also analyze users' emotions and make property suggestions that elicit positive emotions. For example, if the property the user desires is not available, the generative AI can suggest an alternative property. This allows the real estate industry to estimate users' emotions and make property suggestions that elicit positive emotions, thereby improving user satisfaction.

[0076] An emotion-recognizing humanoid robot can make optimal property suggestions based on the user's past search history and preferences. For example, when a user requests, "I'm looking for a 2LDK property near the station," the generation AI analyzes the user's past search history and suggests, "Among the properties you previously searched for, how about a 2LDK property near the station?" The generation AI also analyzes the user's preferences and suggests properties that the user might be interested in. For example, if the property the user desires is not available, the generation AI will suggest similar properties. This makes it possible to improve user satisfaction by making optimal property suggestions based on the user's past search history and preferences.

[0077] An emotion-recognition humanoid robot can suggest the most satisfying property based on emotion estimation data. For example, when a user requests, "I'm looking for a 2LDK property near the station," the generation AI analyzes the emotion estimation data and suggests, "This property is a 5-minute walk from the station and has a spacious living room." The generation AI also suggests the most satisfying property based on the user's emotion estimation data. For example, if the property the user desires is not available, the generation AI suggests an alternative property. This makes it possible to improve user satisfaction by suggesting the most satisfying property based on emotion estimation data.

[0078] In the field of education, emotion-recognizing humanoid robots can estimate a user's emotions and make learning suggestions to elicit positive emotions. For example, when a user requests help with their math homework, the generative AI analyzes the user's emotions and makes positive suggestions, such as, "This problem is easy if you solve it like this." The generative AI can also analyze the user's emotions and make learning suggestions to elicit positive emotions. For example, if the user's desired learning method is not available, the generative AI can suggest an alternative learning method. This makes it possible to estimate a user's emotions in the field of education and make learning suggestions to elicit positive emotions, thereby increasing the user's motivation to learn.

[0079] An emotion-recognizing humanoid robot can suggest optimal learning methods based on the user's past learning history and preferences. For example, when a user requests help with their math homework, the generative AI analyzes the user's past learning history and suggests, "It would be a good idea to solve this problem using the method you learned before." The generative AI also analyzes the user's preferences and suggests learning methods that the user might be interested in. For example, if the user's desired learning method is not available, the generative AI can suggest an alternative learning method. This can increase the user's motivation to learn by suggesting optimal learning methods based on the user's past learning history and preferences.

[0080] An emotion-recognition humanoid robot can suggest the most satisfying learning method based on emotion estimation data. For example, when a user requests "help with my math homework," the generative AI analyzes the emotion estimation data and suggests, "This problem will be easy if you solve it like this." The generative AI also suggests the most satisfying learning method based on the user's emotion estimation data. For example, if the user's desired learning method is not available, the generative AI will suggest an alternative learning method. This can increase the user's motivation to learn by suggesting the most satisfying learning method based on emotion estimation data.

[0081] An emotion-aware humanoid robot can monitor a user's learning progress in real time and adjust the learning plan as needed. For example, when a user requests help with their math homework, the generative AI will monitor the user's learning progress and suggest, "To solve this problem, first review this basics." The generative AI can also monitor the user's learning progress in real time and adjust the learning plan as needed. For example, if the user is struggling with a particular problem, the generative AI will suggest reviewing basic knowledge related to that problem. This allows the system to monitor the user's learning progress in real time and adjust the learning plan as needed, enabling effective learning.

[0082] An emotion-recognizing humanoid robot can provide a customized learning plan tailored to the user's learning style. For example, when a user requests "Help me with my math homework," the generative AI analyzes the user's learning style and suggests, "Let's use a visual explanation to solve this problem." The generative AI can also provide a customized learning plan tailored to the user's learning style. For example, it can provide explanations using diagrams and graphs to visual users, and audio commentary to auditory users. This allows for effective learning by providing a customized learning plan tailored to the user's learning style.

[0083] An emotion-recognition humanoid robot can use its emotion estimation function to provide feedback to increase a user's motivation to learn. For example, when a user requests help with their math homework, the generative AI analyzes the emotion estimation data and provides positive feedback such as, "Your efforts will be rewarded when you solve this problem." The generative AI can also analyze the user's emotions and provide feedback to increase motivation to learn. For example, if the user expresses negative emotions toward a particular problem, the generative AI can provide encouraging words. This allows for effective learning by providing feedback to increase a user's motivation to learn using the emotion estimation function.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The personalized service system can also collect the user's health data and suggest outfits based on their health condition. For example, if the user is connected to a health management app, the generation AI can analyze that data and suggest outfits that suit the user's physical condition. For example, if the user gets tired easily, it can suggest items made from relaxing materials. Also, if the user has allergies, it can suggest items made from materials that do not contain allergens. This can improve user satisfaction by suggesting outfits based on the user's health condition.

[0086] The outfit suggestion unit can estimate the user's emotions and suggest outfits based on those emotions. For example, if a user inputs, "I'm feeling down today," the generation AI analyzes that emotion and suggests outfits in bright colors to lift their spirits. For example, it selects items in bright colors such as yellow or orange. Also, if a user inputs, "Today is a special day, so I want to dress up," the generation AI analyzes that emotion and suggests outfits that are glamorous. For example, it selects dresses and accessories. This makes it possible to improve user satisfaction by suggesting outfits based on the user's emotions.

[0087] The outfit suggestion unit can suggest the best outfit for the user's body type, skin color, and season. For example, when a user uploads an image of themselves, the generation AI analyzes the image and suggests outfits that suit the user's body type. For example, it selects jackets and pants that fit the body type. The generation AI also suggests outfits that match the user's skin color. For example, it selects items in colors that match the skin color. Furthermore, the generation AI suggests outfits that match the season. For example, it suggests items made of warm materials in winter. This makes it possible to improve user satisfaction by suggesting the best outfits for the user's body type, skin color, and season.

[0088] The coordination suggestion unit can suggest coordinations based on the user's past purchase history and social media activity. For example, it can analyze the user's past purchase history and suggest items from the same brand or style. For example, it can suggest new items from a brand that was previously purchased. The generation AI also analyzes the user's social media activity to understand the user's preferences. For example, it can suggest items that match the user's preferences based on items the user has "liked" and posts they have shared. This can improve user satisfaction by suggesting coordinations based on the user's past purchase history and social media activity.

[0089] The inventory confirmation unit can estimate the user's emotions and suggest alternative products if the item is out of stock. For example, if a user asks, "Do you have this shirt in size M?" and the item is out of stock, the generation AI will analyze the user's disappointment and suggest, "Size M is out of stock, but how about size L or a different design?" The generation AI can also analyze the user's emotions and suggest alternative products to alleviate negative emotions. For example, if the item the user wants is out of stock, the generation AI will suggest a similar product. This makes it possible to estimate the user's emotions and suggest alternative products if the item is out of stock, thereby improving user satisfaction.

[0090] The inventory confirmation unit can provide the expected arrival date of a product and the stock status of other stores in real time. For example, if a user asks, "Do you have this shirt in size M?" and it is out of stock, the generation AI will check the expected arrival date of the product and suggest, "Size M is out of stock, but will be in stock next week." The generation AI will also check the stock status of other stores and suggest, "This store is out of stock, but other stores have it in stock." This allows for improved user satisfaction by providing the expected arrival date of a product and the stock status of other stores in real time.

[0091] The inventory confirmation unit can suggest alternative products based on the user's past purchase history and preferences. For example, if a user asks, "Do you have this shirt in size M?" and it is out of stock, the generation AI will analyze the user's past purchase history and suggest, "Size M is out of stock, but how about a new shirt from a brand you previously purchased?" The generation AI also analyzes the user's preferences and suggests new or related products that the user might be interested in. For example, if the product the user wants is out of stock, the generation AI will suggest a similar product. This makes it possible to improve user satisfaction by suggesting alternative products based on the user's past purchase history and preferences.

[0092] The multilingual support unit can estimate the user's emotions and suggest appropriate words and expressions to elicit positive emotions. For example, when a user requests "I am looking for a formal dress" in English, the generation AI analyzes that emotion and suggests "How about this elegant dress?" to elicit positive emotions. The generation AI also analyzes the user's emotions and suggests appropriate words and expressions. For example, if the product the user wants is out of stock, the generation AI will suggest an alternative product using positive language. In this way, by estimating the user's emotions and suggesting appropriate words and expressions to elicit positive emotions, it is possible to improve user satisfaction.

[0093] The multilingual support unit can handle not only the user's native language but also regional dialects and slang. For example, when a user requests "I need a jacket for the winter" in English, the generation AI will take into account the regional dialect and slang and suggest "How about this cozy winter jacket?" The generation AI also handles not only the user's native language but also regional dialects and slang. For example, if the product the user wants is out of stock, the generation AI will suggest an alternative product using regional dialects and slang. This improves user satisfaction by supporting not only the user's native language but also regional dialects and slang.

[0094] The multilingual support unit can make service suggestions that take into account the user's cultural background and customs. For example, when a user makes a request in English saying, "I am looking for a traditional outfit," the generation AI will take that cultural background into account and suggest, "How about this traditional kimono?" The generation AI also makes service suggestions that take into account the user's cultural background and customs. For example, if the product the user wants is out of stock, the generation AI will suggest an alternative product, taking into account the user's cultural background and customs. This makes it possible to improve user satisfaction by making service suggestions that take into account the user's cultural background and customs.

[0095] The data collection unit can collect user emotional data and reflect it in marketing strategies. For example, if a user expresses positive emotions toward a particular product, that product can be promoted with emphasis. For example, products with high emotional scores can be used in advertising. The generation AI also analyzes user emotional data and reflects it in marketing strategies. For example, if a product desired by a user is out of stock, the generation AI can suggest an alternative product based on the emotional data. In this way, by collecting user emotional data and reflecting it in marketing strategies, effective promotions can be achieved.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: Generative AI is a multimodal generative AI model that understands text, images, audio, and video, analyzes the user's request, and provides appropriate services. For example, the generative AI generates a coordinate system based on a prompt containing the user's request. Step 2: The emotion-aware humanoid robot recognizes the user's emotions and provides appropriate feedback. For example, the emotion-aware humanoid robot analyzes the user's facial expressions and voice to infer their emotions. Step 3: The outfit suggestion unit analyzes the user's preferences and past purchase history to suggest the optimal outfit. For example, if a user requests a "casual style," the AI ​​analyzes the request and selects appropriate items. Step 4: The inventory check unit analyzes the store's inventory data in real time to check the stock status. For example, if a user asks, "Do you have this shirt in size M?", the generation AI will check the inventory data and, if it is out of stock, will suggest, "Size M is out of stock, but how about size L or a different design?" Step 5: The substitute product suggestion unit suggests substitute products if they are out of stock. For example, if the product desired by the user is out of stock, the generation AI will suggest similar products. Step 6: The multilingual support section handles multilingual support. For example, if a user makes a request in English or Chinese, the generation AI will understand the request and provide the appropriate service. Step 7: The data collection department collects and analyzes user behavior data and purchase history. For example, it analyzes data such as which items are popular and what time of day people visit the store, and plans effective promotions.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Generative AI and Emotion-recognition humanoid robots and a coordination suggestion unit that analyzes the user's preferences and past purchase history to suggest optimal coordination; The inventory confirmation department analyzes store inventory data in real time to check stock status, an alternative product suggestion unit that suggests an alternative product when the product is out of stock; A multilingual support department that handles multiple languages; A data collection unit that collects and analyzes user behavior data and purchase history. A system characterized by:

2. The coordination suggestion unit Estimating the user's emotions and suggesting outfits based on the emotions 2. The system of claim 1.

3. The inventory confirmation unit Estimate the user's emotions and suggest alternative products if the product is out of stock 2. The system of claim 1.

4. The multilingual support unit Estimate the user's emotions and suggest appropriate words and expressions to elicit positive emotions 2. The system of claim 1.

5. The data collection unit Collecting user sentiment data and reflecting it in marketing strategies 2. The system of claim 1.

6. The emotion-recognition humanoid robot is Estimate the user's emotions at a restaurant and suggest menu items that will elicit positive emotions.

2. The system of claim 1.

7. The emotion-recognition humanoid robot is In the field of education, the system estimates the user's emotions and makes learning suggestions to elicit positive emotions.

2. The system of claim 1.

8. The data collection unit Monitor the effectiveness of your marketing strategies in real time and adjust them as needed 2. The system of claim 1.

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